import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
lowerCamelCase__  =					(720, 1280)  # Height, Width
lowerCamelCase__  =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCamelCase__  =					1 / 100
lowerCamelCase__  =					''''''
lowerCamelCase__  =					''''''
lowerCamelCase__  =					''''''
lowerCamelCase__  =					250


def 				lowerCAmelCase__       (			)      ->None:




  '''simple docstring'''

  _UpperCamelCase      ,						_UpperCamelCase           =    get_dataset(a__    ,     a__ )
  for index in range(a__ ):
    _UpperCamelCase           =    random.sample(range(len(a__ ) )    ,     4 )
    _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    update_image_and_anno(
        a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     filter_scale=a__    ,     )

    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    _UpperCamelCase           =    random_chars(32 )
    _UpperCamelCase           =    path.split(os.sep )[-1].rsplit("."    ,     1 )[0]
    _UpperCamelCase           =    f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
    cva.imwrite(f'{file_root}.jpg'    ,     a__    ,     [cva.IMWRITE_JPEG_QUALITY, 85] )
    print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}' )
    _UpperCamelCase           =    []
    for anno in new_annos:
      _UpperCamelCase           =    anno[3] - anno[1]
      _UpperCamelCase           =    anno[4] - anno[2]
      _UpperCamelCase           =    anno[1] + width / 2
      _UpperCamelCase           =    anno[2] + height / 2
      _UpperCamelCase           =    f'{anno[0]} {x_center} {y_center} {width} {height}'
      annos_list.append(a__ )
    with open(f'{file_root}.txt'    ,     "w" ) as outfile:
      outfile.write("\n".join(line for line in annos_list ) )


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->tuple[list, list]:




  '''simple docstring'''

  _UpperCamelCase           =    []
  _UpperCamelCase           =    []
  for label_file in glob.glob(os.path.join(a__    ,     "*.txt" ) ):
    _UpperCamelCase           =    label_file.split(os.sep )[-1].rsplit("."    ,     1 )[0]
    with open(a__ ) as in_file:
      _UpperCamelCase           =    in_file.readlines()
    _UpperCamelCase           =    os.path.join(a__    ,     f'{label_name}.jpg' )

    _UpperCamelCase           =    []
    for obj_list in obj_lists:
      _UpperCamelCase           =    obj_list.rstrip("\n" ).split(" " )
      _UpperCamelCase           =    float(obj[1] ) - float(obj[3] ) / 2
      _UpperCamelCase           =    float(obj[2] ) - float(obj[4] ) / 2
      _UpperCamelCase           =    float(obj[1] ) + float(obj[3] ) / 2
      _UpperCamelCase           =    float(obj[2] ) + float(obj[4] ) / 2

      boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
    if not boxes:
      continue
    img_paths.append(a__ )
    labels.append(a__ )
  return img_paths, labels


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     a__ = 0.0    ,     )      ->tuple[list, list, str]:




  '''simple docstring'''

  _UpperCamelCase           =    np.zeros([output_size[0], output_size[1], 3]    ,     dtype=np.uinta )
  _UpperCamelCase           =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  _UpperCamelCase           =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  _UpperCamelCase           =    int(scale_x * output_size[1] )
  _UpperCamelCase           =    int(scale_y * output_size[0] )

  _UpperCamelCase           =    []
  _UpperCamelCase           =    []
  for i, index in enumerate(a__ ):
    _UpperCamelCase           =    all_img_list[index]
    path_list.append(a__ )
    _UpperCamelCase           =    all_annos[index]
    _UpperCamelCase           =    cva.imread(a__ )
    if i == 0:  # top-left
      _UpperCamelCase           =    cva.resize(a__    ,     (divid_point_x, divid_point_y) )
      _UpperCamelCase           =    img
      for bbox in img_annos:
        _UpperCamelCase           =    bbox[1] * scale_x
        _UpperCamelCase           =    bbox[2] * scale_y
        _UpperCamelCase           =    bbox[3] * scale_x
        _UpperCamelCase           =    bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
    elif i == 1:  # top-right
      _UpperCamelCase           =    cva.resize(a__    ,     (output_size[1] - divid_point_x, divid_point_y) )
      _UpperCamelCase           =    img
      for bbox in img_annos:
        _UpperCamelCase           =    scale_x + bbox[1] * (1 - scale_x)
        _UpperCamelCase           =    bbox[2] * scale_y
        _UpperCamelCase           =    scale_x + bbox[3] * (1 - scale_x)
        _UpperCamelCase           =    bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
    elif i == 2:  # bottom-left
      _UpperCamelCase           =    cva.resize(a__    ,     (divid_point_x, output_size[0] - divid_point_y) )
      _UpperCamelCase           =    img
      for bbox in img_annos:
        _UpperCamelCase           =    bbox[1] * scale_x
        _UpperCamelCase           =    scale_y + bbox[2] * (1 - scale_y)
        _UpperCamelCase           =    bbox[3] * scale_x
        _UpperCamelCase           =    scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
    else:  # bottom-right
      _UpperCamelCase           =    cva.resize(
          a__    ,     (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
      _UpperCamelCase           =    img
      for bbox in img_annos:
        _UpperCamelCase           =    scale_x + bbox[1] * (1 - scale_x)
        _UpperCamelCase           =    scale_y + bbox[2] * (1 - scale_y)
        _UpperCamelCase           =    scale_x + bbox[3] * (1 - scale_x)
        _UpperCamelCase           =    scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
  if filter_scale > 0:
    _UpperCamelCase           =    [
        anno
        for anno in new_anno
        if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
    ]

  return output_img, new_anno, path_list[0]


def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  assert number_char > 1, "The number of character should greater than 1"
  _UpperCamelCase           =    ascii_lowercase + digits
  return "".join(random.choice(a__ ) for _ in range(a__ ) )


if __name__ == "__main__":
   main()
   print('''DONE ✅''')





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax

   from transformers.models.big_bird.modeling_flax_big_bird import (
       FlaxBigBirdForCausalLM,
       FlaxBigBirdForMaskedLM,
       FlaxBigBirdForMultipleChoice,
       FlaxBigBirdForPreTraining,
       FlaxBigBirdForQuestionAnswering,
       FlaxBigBirdForSequenceClassification,
       FlaxBigBirdForTokenClassification,
       FlaxBigBirdModel,
   )




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def __init__(		self				:   List[Any]							, lowercase_				:   Union[str, Any]							, lowercase_				:   Union[str, Any]=2							, lowercase_				:   Dict=56							, lowercase_				:   str=True							, lowercase_				:   Optional[Any]=True							, lowercase_				:   List[Any]=True							, lowercase_				:   Union[str, Any]=True							, lowercase_				:   Any=99							, lowercase_				:   Optional[int]=32							, lowercase_				:   Tuple=2							, lowercase_				:   int=2							, lowercase_				:   List[str]=7							, lowercase_				:   Any="gelu_new"							, lowercase_				:   List[str]=0.1							, lowercase_				:   str=0.1							, lowercase_				:   List[Any]=512							, lowercase_				:   List[str]=16							, lowercase_				:   Optional[int]=2							, lowercase_				:   Union[str, Any]=0.02							, lowercase_				:   Union[str, Any]=4							, lowercase_				:   Union[str, Any]="block_sparse"							, lowercase_				:   Tuple=True							, lowercase_				:   Dict=False							, lowercase_				:   Dict=2							, lowercase_				:   Dict=3							, )						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    parent
   _UpperCamelCase           =    batch_size
   _UpperCamelCase           =    seq_length
   _UpperCamelCase           =    is_training
   _UpperCamelCase           =    use_attention_mask
   _UpperCamelCase           =    use_token_type_ids
   _UpperCamelCase           =    use_labels
   _UpperCamelCase           =    vocab_size
   _UpperCamelCase           =    hidden_size
   _UpperCamelCase           =    num_hidden_layers
   _UpperCamelCase           =    num_attention_heads
   _UpperCamelCase           =    intermediate_size
   _UpperCamelCase           =    hidden_act
   _UpperCamelCase           =    hidden_dropout_prob
   _UpperCamelCase           =    attention_probs_dropout_prob
   _UpperCamelCase           =    max_position_embeddings
   _UpperCamelCase           =    type_vocab_size
   _UpperCamelCase           =    type_sequence_label_size
   _UpperCamelCase           =    initializer_range
   _UpperCamelCase           =    num_choices

   _UpperCamelCase           =    rescale_embeddings
   _UpperCamelCase           =    attention_type
   _UpperCamelCase           =    use_bias
   _UpperCamelCase           =    block_size
   _UpperCamelCase           =    num_random_blocks







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size)

   _UpperCamelCase           =    None
   if self.use_attention_mask:
     _UpperCamelCase           =    random_attention_mask([self.batch_size, self.seq_length])

   _UpperCamelCase           =    None
   if self.use_token_type_ids:
     _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size)

   _UpperCamelCase           =    BigBirdConfig(
       vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, is_decoder=lowercase_							, initializer_range=self.initializer_range							, attention_type=self.attention_type							, block_size=self.block_size							, num_random_blocks=self.num_random_blocks							, use_bias=self.use_bias							, rescale_embeddings=self.rescale_embeddings							, )

   return config, input_ids, token_type_ids, attention_mask







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.prepare_config_and_inputs()
   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    config_and_inputs
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "attention_mask": attention_mask,
   }
   return config, inputs_dict




@require_flax
class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					(
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 __A			       =					False
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    FlaxBigBirdModelTester(self)







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	List[str]:


   """simple docstring"""







   super().test_from_pretrained_save_pretrained()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Any)						->	int:


   """simple docstring"""







   super().test_from_pretrained_with_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   str)						->	Union[str, Any]:


   """simple docstring"""







   super().test_hidden_states_output()







 @slow
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[int]:


   """simple docstring"""







   for model_class_name in self.all_model_classes:
     _UpperCamelCase           =    model_class_name.from_pretrained("google/bigbird-roberta-base")
     self.assertIsNotNone(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	str:


   """simple docstring"""







   if self.test_attn_probs:
     super().test_attention_outputs()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Dict:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       _UpperCamelCase           =    self._prepare_for_class(lowercase_							, lowercase_)
       _UpperCamelCase           =    model_class(lowercase_)

       @jax.jit
       def model_jitted(lowercase_				:   Dict							, lowercase_				:   List[Any]=None							, **lowercase_				:   Tuple):
         return model(input_ids=lowercase_							, attention_mask=lowercase_							, **lowercase_)

       with self.subTest("JIT Enabled"):
         _UpperCamelCase           =    model_jitted(**lowercase_).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           _UpperCamelCase           =    model_jitted(**lowercase_).to_tuple()

       self.assertEqual(len(lowercase_)							, len(lowercase_))
       for jitted_output, output in zip(lowercase_							, lowercase_):
         self.assertEqual(jitted_output.shape							, output.shape)







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   str=1e-5							, lowercase_				:   int="outputs"							, lowercase_				:   List[str]=None)						->	Tuple:


   """simple docstring"""







   if name.startswith("outputs.attentions"):
     return
   else:
     super().check_pt_flax_outputs(lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)




